The goal of this project is to improve the efficacy of PET imaging through the development of novel image reconstruction methods and data analysis tools. PET is a molecular imaging modality that is capable of imaging physiological and biochemical processes directly in humans and animals by labeling biomolecules of interests with positron emitters. It has wide applications in clinical diagnosis and biological research, includin oncology, cardiology, neuroscience, and studies of various human diseases using animal models. PET/CT with [18F]fluorodeoxyglucose (FDG) is increasingly being used for staging, restaging and treatment monitoring for cancer patients with different types of tumors. However, current FDG-PET provides a low sensitivity to detect micrometastases and small tumor infiltrated lymph nodes. Therefore, improving the quality of PET images will have a profound impact on modern medicine. This proposal builds upon our previous work on patient-adaptive penalized maximum likelihood image reconstruction and dynamic PET imaging. The objective of this proposal is to evaluate the promising image reconstruction methods that we have developed using patient data with histology-verified ground truth and to enhance the robustness and performance of these methods through task- specific optimization and adding motion compensation ability. The four specific aims are (i) Evaluation of patient-adaptive PML reconstruction using breast cancer patients with histologically verified ground truth, (ii) Development of patient-adaptive dynamic PET image reconstruction, (iii) Development of a motion-compensated dynamic PET image reconstruction method, and (iv) Validation of the motion-compensated reconstruction using biopsy-proven lung cancer patient data. The success of this research will have a significant and positive impact on the clinical application of PET imaging.